




'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase_									=		logging.get_logger(__name__)

lowerCamelCase_									=		{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
lowerCamelCase_									=		[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]
def 					SCREAMING_SNAKE_CASE_					(				__A							:					Optional[int]			,	__A							:					Optional[Any]			,	__A							:					Any			,	__A							:					List[str]			,	__A							:					Dict    )					->					List[Any]:
   for attribute in key.split("."    ):
      _SCREAMING_SNAKE_CASE			= getattr(snake_case__			,	snake_case__    )

   if weight_type is not None:
      _SCREAMING_SNAKE_CASE			= getattr(snake_case__			,	snake_case__    ).shape
   else:
      _SCREAMING_SNAKE_CASE			= hf_pointer.shape

   assert hf_shape == value.shape, (
       f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
       f""" {value.shape} for {full_name}"""
   )

   if weight_type == "weight":
      _SCREAMING_SNAKE_CASE			= value
   elif weight_type == "weight_g":
      _SCREAMING_SNAKE_CASE			= value
   elif weight_type == "weight_v":
      _SCREAMING_SNAKE_CASE			= value
   elif weight_type == "bias":
      _SCREAMING_SNAKE_CASE			= value
   else:
      _SCREAMING_SNAKE_CASE			= value

   logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""    )
def 					SCREAMING_SNAKE_CASE_					(				__A							:					Optional[Any]			,	__A							:					Tuple    )					->					str:
   _SCREAMING_SNAKE_CASE			= []
   _SCREAMING_SNAKE_CASE			= fairseq_model.state_dict()

   _SCREAMING_SNAKE_CASE			= hf_model.feature_extractor

   # if encoder has different dim to decoder -> use proj_weight
   _SCREAMING_SNAKE_CASE			= None

   for name, value in fairseq_dict.items():
      _SCREAMING_SNAKE_CASE			= False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	hf_model.config.feat_extract_norm == "group"			,	)
         _SCREAMING_SNAKE_CASE			= True
      elif name.split("."    )[0] == "proj":
         _SCREAMING_SNAKE_CASE			= fairseq_model.proj
         _SCREAMING_SNAKE_CASE			= True
      else:
         for key, mapped_key in MAPPING.items():
            if key in name or key.split("w2v_model."    )[-1] == name.split("."    )[0]:
               _SCREAMING_SNAKE_CASE			= True
               if "*" in mapped_key:
                  _SCREAMING_SNAKE_CASE			= name.split(snake_case__    )[0].split("."    )[-2]
                  _SCREAMING_SNAKE_CASE			= mapped_key.replace("*"			,	snake_case__    )
               if "weight_g" in name:
                  _SCREAMING_SNAKE_CASE			= "weight_g"
               elif "weight_v" in name:
                  _SCREAMING_SNAKE_CASE			= "weight_v"
               elif "bias" in name:
                  _SCREAMING_SNAKE_CASE			= "bias"
               elif "weight" in name:
                  _SCREAMING_SNAKE_CASE			= "weight"
               else:
                  _SCREAMING_SNAKE_CASE			= None
               set_recursively(snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__    )
            continue
      if not is_used:
         unused_weights.append(snake_case__    )

   logger.warning(f"""Unused weights: {unused_weights}"""    )

   return proj_weight
def 					SCREAMING_SNAKE_CASE_					(				__A							:					Tuple			,	__A							:					str			,	__A							:					List[Any]			,	__A							:					str			,	__A							:					Optional[int]    )					->					int:
   _SCREAMING_SNAKE_CASE			= full_name.split("conv_layers."    )[-1]
   _SCREAMING_SNAKE_CASE			= name.split("."    )
   _SCREAMING_SNAKE_CASE			= int(items[0]    )
   _SCREAMING_SNAKE_CASE			= int(items[1]    )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         _SCREAMING_SNAKE_CASE			= value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""    )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         _SCREAMING_SNAKE_CASE			= value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""    )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         _SCREAMING_SNAKE_CASE			= value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         _SCREAMING_SNAKE_CASE			= value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
   else:
      unused_weights.append(snake_case__    )
def 					SCREAMING_SNAKE_CASE_					(				__A							:					str    )					->					Optional[Any]:
   _SCREAMING_SNAKE_CASE,			_SCREAMING_SNAKE_CASE			= emb.weight.shape
   _SCREAMING_SNAKE_CASE			= nn.Linear(snake_case__			,	snake_case__			,	bias=snake_case__    )
   _SCREAMING_SNAKE_CASE			= emb.weight.data
   return lin_layer
def 					SCREAMING_SNAKE_CASE_					(				__A							:					int    )					->					List[Any]:
   with open(snake_case__			,	"r"			,	encoding="utf-8"    ) as f:
      _SCREAMING_SNAKE_CASE			= f.readlines()
      _SCREAMING_SNAKE_CASE			= [line.split(" "    )[0] for line in lines]

   _SCREAMING_SNAKE_CASE			= len(snake_case__    )

   _SCREAMING_SNAKE_CASE			= {
       "<s>": 0,
       "<pad>": 1,
       "</s>": 2,
       "<unk>": 3,
   }

   vocab_dict.update(dict(zip(snake_case__			,	range(4			,	num_words + 4    )    )    )    )
   return vocab_dict
@torch.no_grad()
def 					SCREAMING_SNAKE_CASE_					(				__A							:					str			,	__A							:					Dict			,	__A							:					Dict			,	__A							:					Tuple			,	__A							:					str			,	__A							:					Union[str, Any]			,	__A							:					List[Any]			,	)					->					List[str]:
   _SCREAMING_SNAKE_CASE			= WavaVecaConfig.from_pretrained(snake_case__    )
   _SCREAMING_SNAKE_CASE			= SpeechaTextaConfig.from_pretrained(
       snake_case__			,	vocab_size=snake_case__			,	decoder_layers=snake_case__			,	do_stable_layer_norm=snake_case__    )

   _SCREAMING_SNAKE_CASE			= WavaVecaFeatureExtractor(
       feature_size=1			,	sampling_rate=1_60_00			,	padding_value=0			,	do_normalize=snake_case__			,	return_attention_mask=snake_case__			,	)

   _SCREAMING_SNAKE_CASE,			_SCREAMING_SNAKE_CASE,			_SCREAMING_SNAKE_CASE			= fairseq.checkpoint_utils.load_model_ensemble_and_task(
       [checkpoint_path]			,	arg_overrides={"data": "/".join(dict_path.split("/"    )[:-1]    )}    )
   _SCREAMING_SNAKE_CASE			= model[0].eval()

   # set weights for wav2vec2 encoder
   _SCREAMING_SNAKE_CASE			= WavaVecaModel(snake_case__    )
   _SCREAMING_SNAKE_CASE			= recursively_load_weights_wavaveca(model.encoder			,	snake_case__    )

   _SCREAMING_SNAKE_CASE			= SpeechaTextaForCausalLM(snake_case__    )
   _SCREAMING_SNAKE_CASE,			_SCREAMING_SNAKE_CASE			= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()			,	strict=snake_case__    )

   # set output linear layer
   unexpected_keys.remove("embed_out"    )
   _SCREAMING_SNAKE_CASE			= nn.Parameter(model.decoder.embed_out.detach()    )

   # layer norm is init to identity matrix so leaving it is fine
   logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""    )
   logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""    )

   _SCREAMING_SNAKE_CASE			= SpeechEncoderDecoderModel(encoder=snake_case__			,	decoder=snake_case__    )
   _SCREAMING_SNAKE_CASE			= False

   # add projection layer
   _SCREAMING_SNAKE_CASE			= nn.Parameter(projection_layer.weight    )
   _SCREAMING_SNAKE_CASE			= nn.Parameter(projection_layer.bias    )

   _SCREAMING_SNAKE_CASE			= create_vocab_dict(snake_case__    )

   with open(os.path.join(snake_case__			,	"vocab.json"    )			,	"w"    ) as fp:
      json.dump(snake_case__			,	snake_case__    )

   _SCREAMING_SNAKE_CASE			= SpeechaTextaTokenizer(os.path.join(snake_case__			,	"vocab.json"    )    )
   tokenizer.save_pretrained(snake_case__    )

   _SCREAMING_SNAKE_CASE			= hf_wavavec.config.to_dict()
   _SCREAMING_SNAKE_CASE			= tokenizer.pad_token_id
   _SCREAMING_SNAKE_CASE			= tokenizer.bos_token_id
   _SCREAMING_SNAKE_CASE			= tokenizer.eos_token_id
   _SCREAMING_SNAKE_CASE			= "speech_to_text_2"
   _SCREAMING_SNAKE_CASE			= "wav2vec2"

   _SCREAMING_SNAKE_CASE			= SpeechEncoderDecoderConfig.from_dict(snake_case__    )

   hf_wavavec.save_pretrained(snake_case__    )
   feature_extractor.save_pretrained(snake_case__    )


if __name__ == "__main__":
 lowerCamelCase_									=		argparse.ArgumentParser()
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
 parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
 parser.add_argument(
     '--encoder_config_path',
     default='facebook/wav2vec2-large-lv60',
     type=str,
     help='Path to hf encoder wav2vec2 checkpoint config',
 )
 parser.add_argument(
     '--decoder_config_path',
     default='facebook/s2t-small-mustc-en-fr-st',
     type=str,
     help='Path to hf decoder s2t checkpoint config',
 )
 parser.add_argument('--vocab_size', default=1_02_24, type=int, help='Vocab size of decoder')
 parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

 lowerCamelCase_									=		parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     vocab_size=args.vocab_size,
     num_decoder_layers=args.num_decoder_layers,
 )







"""simple docstring"""




import warnings

from .generation import TFGenerationMixin






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				# warning at import time
				warnings.warn(
				    "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
				    "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."		,   UpperCAmelCase__		,   )



